Comparing surface-soil moisture from the SMOS mission and the ORCHIDEE land-surface model over the Iberian Peninsula

The aim of this study is to compare the surface soil moisture (SSM) retrieved from ESA's Soil Moisture and Ocean Salinity mission (SMOS) with the output of the ORCHIDEE (ORganising Carbon and Hydrology In Dynamic EcosystEm) land surface model forced with two distinct atmospheric data sets for the period 2010 to 2012. The comparison methodology is first established over the REMEDHUS (Red de Estaciones de MEDición de la Humedad def Suelo) soil moisture measurement network, a 30 by 40. km catchment located in the central part of the Duero basin, then extended to the whole Iberian Peninsula (IP). The temporal correlation between the in-situ, remotely sensed and modelled SSM are satisfactory (r. >. 0.8). The correlation between remotely sensed and modelled SSM also holds when computed over the IP. Still, by using spectral analysis techniques, important disagreements in the effective inertia of the corresponding moisture reservoir are found. This is reflected in the spatial correlation over the IP between SMOS and ORCHIDEE SSM estimates, which is poor (¿. ~. 0.3). A single value decomposition (SVD) analysis of rainfall and SSM shows that the co-varying patterns of these variables are in reasonable agreement between both products. Moreover the first three SVD soil moisture patterns explain over 80% of the SSM variance simulated by the model while the explained fraction is only 52% of the remotely sensed values. These results suggest that the rainfall-driven soil moisture variability may not account for the poor spatial correlation between SMOS and ORCHIDEE products.Peer ReviewedPostprint (published version

Forest Structure Characterization From SAR Tomography at L-Band

Synthetic aperture radar (SAR) remote sensing configurations are able to provide continuous measurements on global scales sensitive to the vertical structure of forests with a high spatial and temporal resolution. Furthermore, the development of tomographic SAR techniques allows the reconstruction of the three-dimensional (3-D) radar reflectivity opening the door for 3-D forest monitoring. However, the link between 3-D radar reflectivity and 3-D forest structure is not yet established. In this sense, this paper introduced a framework that allows a qualitative and quantitative interpretation of physical forest structure from tomographic SAR data at L-band. For this, forest structure is parameterized into a set of a horizontal and a vertical structure index. From inventory data, both indices can be derived from the spatial distribution and the dimensions of the trees. Similarly, two structure indices are derived from the 3-D spatial distribution of the local maxima of the reconstructed 3-D radar reflectivity profiles at L-band. The proposed methodology is tested by means of experimental tomographic L-band data acquired over the temperate forest site of Traunstein in Germany. The obtained horizontal and vertical structure indices are validated against the corresponding estimates obtained from inventory measurements and against the same indices derived from the vertical profiles of airborne Lidar data. The high correlation between the forest structure indices obtained from these three different data sources (expressed by correlation coefficients between 0.75 and 0.87) indicates the potential of the proposed framework

A Novel Strategy for Radar Imaging Based on Compressive Sensing

Radar data have already proven to be compressible with no significant losses for most of the applications in which it is used. In the framework of information theory, the compressibility of a signal implies that it can be decomposed onto a reduced set of basic elements. Since the same quantity of information is carried by the original signal and its decomposition, it can be deduced that a certain degree of redundancy exists in the explicit representation. According to the theory of compressive sensing (CS), due to this redundancy, it is possible to infer an accurate representation of an unknown compressible signal through a highly incomplete set of measurements. Based on this assumption, this paper proposes a novel method for the focusing of raw data in the framework of radar imaging. The technique presented is introduced as an alternative option to the traditional matched filtering, and it suggests that the new modes of acquisition of data are more efficient in orbital configurations. In this paper, this method is first tested on 1-D simulated signals, and results are discussed. An experiment with synthetic aperture radar (SAR) raw data is also described. Its purpose is to show the potential of CS applied to SAR systems. In particular, we show that an image can be reconstructed, without the loss of resolution, after dropping a large percentage of the received pulses, which would allow the implementation of wide-swath modes without reducing the azimuth resolution

Towards Forest Structure Characteristics Retrieval from SAR Tomographic Profiles

SAR Tomography has proven to be a unique tool for the retrieval of 3D structure information from forest scenarios: it can reveal different scattering mechanisms at different heights. However, the translation of these measurements into relevant forest structure information is not straightforward and research is still ongoing. In this direction, this paper suggest a framework for the estimation of forest structure from a SAR tomography scheme based on a low number of single pass coherences. Vertical reflectivity profiles are estimated by means of Compressive Sensing Imaging techniques. Two complementary descriptors are then suggested accounting for the simultaneous vertical and horizontal spatial variability of the scene. Their ability to reflect a characteristic structure behavior for the different types of forest considered is analyzed in simulated and real scenarios

Observing the Forest from Lidar and Radar Remote Sensing

Lidar and radar are remote sensing systems that have proven their potential to accurately monitor forest structure characteristics. Both are active systems. They send their own pulses and measure the time elapsed between the transmission of the pulse and its reception after it has been reflected in a scatterer. However, Lidar employs laser light, while radar operates with microwaves, which allows the latter to operate under any weather condition. The viewing geometry is nadir and side looking for the Lidar and the radar respectively, among other differences. Mainly due to the availability of commercial systems, contrarily to radar, Lidar has been extensively employed by forest scientists and practitioners in the last decade. The analysis in parallel of both systems can help to introduce the use of radar data to the ecology community and to identify complementarities between them. This is particularly relevant in views of the forthcoming space missions, such as JEDI and Tandem-L

3D forest structure estimation from sar tomography by means of a full rank polarimetric inversion based on compressive sensing

SAR tomography (TomoSAR) techniques allow a direct 3D imaging by exploiting angular diversity with different passes of the sensor. One of the main drawbacks of SAR tomography is that the estimation of the vertical reflectivity profile has to be performed through a limited set of multibaseline acquisitions, which requires solving a highly underdetermined system of equations. In TomoSAR literature, the Capon and the Fourier beamforming spectral estimators are widely employed. As an alternative, the application of Compressive Sensing (CS) techniques to the estimation of forest profiles has been recently introduced. In this paper, a different algorithm based on CS is proposed. It performs a full rank polarimetric inversion, allowing thus an estimation of the 3D coherency matrices. To study the full rank polarimetric TomoSAR inversion, a temporal series of airborne data is used. The results of the 3D polarimetric inversion will be contrasted to in situ measurements and LIDAR data

Forest Structure Estimation by means of TomoSAR in front of weather and seasonal variability

When observing a scene, a radar system is sensitive to the morphology of the scatterers, but also to their dielectric properties. Therefore, in the framework of forest monitoring, the presence or absence of leaves, the daily and seasonal water cycle in the different structural elements of the tree, as well as rain may have an influence on the 3D reflectivity retrieved by a TomoSAR system at L-band. However, a consistent forest structure estimation can not vary with these non structural effects. Therefore, a measure of forest structure from TomoSAR data at L-band has been suggested, which discriminates in the 3D reflectivity profiles the parameters only affected by structure related properties. The stability of this estimation is checked here on a temporal series of an airborne campaign, carried out by DLR over a test site in the South East of Germany

Assessment of Tomographic SAR Processing Techniques for Forest Structure Estimation

The future SAR missions such as BIOMASS and Tandem-L will exploit the potential of Synthetic Aperture Radar Tomography to extract 3D forest structure information. Several algorithms can be applied for TomoSAR imaging. This paper analyses the performance of two non-parametric algorithms, Capon Beamforming and Compressive Sensing (CS), for forest structure applications, through a set of simulations reflecting different forest scenarios (distribution of canopy layers and temporal decorrealtion) and system parameters (baseline distribution, multilook, and noise). Results show that CS is in general more stable than Capon in front of system and scene variability, but may be more affected by artefacts